A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
VIR model improves regression accuracy and uncertainty estimation for imbalanced data.
Mix-IRLS solves imbalanced mixed linear regression problems efficiently.
Proposes a new data augmentation method for imbalanced datasets in both classification and regression.
Paper introduces a new method to improve learning on imbalanced regression problems.
LDAO addresses imbalanced regression by learning local distribution structures.
A new method for handling imbalanced data in regression models.
Study investigates how preprocessing, feature selection, and model selection affect performance on imbalanced genetic data.
Paper studies SERA's effectiveness in optimizing imbalanced regression models.
PARIS reduces imbalanced regression datasets by pruning uninformative samples.
Logistic regression is a widely used method in several fields. When applying logistic regression to imbalanced data, for which majority classes dominate over minority classes, all class labels are estimated as `majority class.' In this article, we use an F-measure optimization method to improve the performance of logis…
Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discret…
The logistic regression model is known to converge to a Poisson point process model if the binary response tends to infinitely imbalanced. In this paper, it is shown that this phenomenon is universal in a wide class of link functions on binomial regression. The proof relies on the extreme value theory. For the logit, p…
New deep learning method handles rare and imbalanced events in time series.
Study tackles imbalanced data in car insurance claims prediction.
The paper develops a statistical theory explaining overfitting in imbalanced classification.
The paper extends logistic regression for unbounded majority classes and derives asymptotic properties.
This paper improves binary classification methods beyond accuracy, especially in imbalanced datasets.
Deep SMOTE improves SMOTE's stability and accuracy in imbalanced classification.
Proposes GMOTE for better handling imbalanced data.
We introduce a discriminative regression approach to supervised classification in this paper. It estimates a representation model while accounting for discriminativeness between classes, thereby enabling accurate derivation of categorical information. This new type of regression models extends existing models such as r…
Equity-Directed Bootstrapping improves model performance across groups in imbalanced datasets.
Paper proposes fully Bayesian approach for RVM classification, improving accuracy especially in imbalanced data.
In medical domain, data features often contain missing values. This can create serious bias in the predictive modeling. Typical standard data mining methods often produce poor performance measures. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values.…
learn2mix trains neural nets faster by adjusting class proportions dynamically.
Matrix sketching is a recently developed data compression technique. An input matrix A is efficiently approximated with a smaller matrix B, so that B preserves most of the properties of A up to some guaranteed approximation ratio. In so doing numerical operations on big data sets become faster. Sketching algorithms gen…
In this paper, we introduce a new approach to multiclass classification problem. We decompose the problem into a series of regression tasks, that are solved with CART trees. The proposed method works significantly faster than state-of-the-art solutions while giving the same level of model quality. The algorithm is also…
Classifiers trained on data sets possessing an imbalanced class distribution are known to exhibit poor generalisation performance. This is known as the imbalanced learning problem. The problem becomes particularly acute when we consider incremental classifiers operating on imbalanced data streams, especially when the l…
Study shows imbalanced labels can be beneficial but not always in class-imbalanced learning.
Meta Additive Model learns auto-weighting for robust sparse learning.
In supervised machine learning for author name disambiguation, negative training data are often dominantly larger than positive training data. This paper examines how the ratios of negative to positive training data can affect the performance of machine learning algorithms to disambiguate author names in bibliographic …
Theoretical and empirical study on SMOTE rebalancing strategy for imbalanced data.
Private business schools in India face a common problem of selecting quality students for their MBA programs to achieve the desired placement percentage. Generally, such data sets are biased towards one class, i.e., imbalanced in nature. And learning from the imbalanced dataset is a difficult proposition. This paper pr…
Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data. We develop a latent variable model that can cope with imbalanced data by dividing the latent space into a shared space…
Optimal downsampling improves GLM performance in imbalanced classification.
SIGTRON improves classification accuracy for imbalanced datasets.
We aim at developing and improving the imbalanced business risk modeling via jointly using proper evaluation criteria, resampling, cross-validation, classifier regularization, and ensembling techniques. Area Under the Receiver Operating Characteristic Curve (AUC of ROC) is used for model comparison based on 10-fold cro…
When the training data in a two-class classification problem is overwhelmed by one class, most classification techniques fail to correctly identify the data points belonging to the underrepresented class. We propose Similarity-based Imbalanced Classification (SBIC) that learns patterns in the training data based on an …
TSVQR captures heterogeneous and asymmetric data using quantile regression.
This study is motivated by the magnitude of the problem of Louisiana high school dropout and its negative impacts on individual and public well-being. Our goal is to predict students who are at risk of high school dropout, by examining Louisiana administrative dataset. Due to the imbalanced nature of the dataset, imbal…
Paper reviews and compares methods for handling imbalanced data.
Study challenges the necessity of data augmentation for improving predictions on imbalanced text datasets.
SkewPNN uses probabilistic neural networks with skew-normal kernels to improve classification of imbalanced data.
Improves comparison of F-measures for imbalanced datasets.
Data in real-world application often exhibit skewed class distribution which poses an intense challenge for machine learning. Conventional classification algorithms are not effective in the case of imbalanced data distribution, and may fail when the data distribution is highly imbalanced. To address this issue, we prop…
Imbalanced datasets are ubiquitous. Classification performance on imbalanced datasets is generally poor for the minority class as the classifier cannot learn decision boundaries well. However, in sensitive applications like fraud detection, medical diagnosis, and spam identification, it is extremely important to classi…
PROTOCOL tackles imbalanced multi-view clustering by enhancing contrastive learning.
This paper discusses financial fraud detection in imbalanced dataset using homogeneous and non-homogeneous Poisson processes. The probability of predicting fraud on the financial transaction is derived. Applying our methodology to the financial dataset shows a better predicting power than a baseline approach, especiall…
In this study, we consider classification problems based on neural networks in data-imbalanced environment. Learning from an imbalanced data set is one of the most important and practical problems in the field of machine learning. A weighted loss function based on cost-sensitive approach is a well-known effective metho…